Treatment of Extended Kalman Filter Implementations for the Gyroless Star Tracker

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model's high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target's maneuvering turn. Second, an improved Sage-Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination

For the orbit operation of spacecraft, due to environmental factors, a star sensor installed on the spacecraft must have data censoring, which greatly reduces the attitude determination ability of the traditional combined-attitude-determination algorithm. To address this problem, this paper proposes an algorithm for high-precision attitude estimation based on a Tobit unscented Kalman filter. This is on the basis of establishing the nonlinear state equation of the integrated star sensor and gyroscope navigation system. The measurement update process of the unscented Kalman filter is improved. The Tobit model is used to describe the gyroscope drift when the star sensor fails. The latent measurement values are calculated using the probability statistics, and the measurement error covariance expression is derived. The proposed design is verified via computer simulations. When the star sensor fails for 15 min, the accuracy of the Tobit unscented Kalman filter based on the Tobit model is improved by approximately 90% compared to the unscented Kalman filter. Based on the results, the proposed filter can effectively estimate the error caused by the gyro drift, and the method is effective and feasible, provided there is theoretical support for the engineering practice.

Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter

High-precision and robust localization is critical for intelligent vehicle and transportation systems, while the sensor signal loss or variance could dramatically affect the localization performance. The vehicle localization problem in an environment with Global Navigation Satellite System (GNSS) signal errors is investigated in this study. The error state Kalman filtering (ESKF) and Rauch-Tung-Striebel (RTS) smoother are integrated using the data from Inertial Measurement Unit (IMU) and GNSS sensors. A segmented RTS smoothing algorithm is proposed in order to estimate the error state, which is typically close to zero and mostly linear, which allows more accurate linearization and improved state estimation accuracy. The proposed algorithm is evaluated using simulated GNSS signals with and without signal errors. The simulation results demonstrate its superior accuracy and stability for state estimation. The designed ESKF algorithm yielded an approximate 3% improvement in long straight line and turning scenarios compared to classical EKF algorithm. Additionally, the ESKF-RTS algorithm exhibited a 10% increase in the localization accuracy compared to the ESKF algorithm. In the double turning scenarios, the ESKF algorithm resulted in an improvement of about 50% in comparison to the EKF algorithm, while the ESKF-RTS algorithm improved by about 50% compared to the ESKF algorithm. These results indicated that the proposed ESKF-RTS algorithm is more robust and provides more accurate localization.